Mailman 3 February 2019 - NumPy-Discussion

mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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Invalid value encoutered : how to prevent numpy.where to do this?
by Eric Emsellem 18 Feb '23

18 Feb '23

Dear all, I have a code using lots of "numpy.where" to make some constrained calculations as in: data = arange(10) result = np.where(data == 0, 0., 1./data) # or data1 = arange(10) data2 = arange(10)+1.0 result = np.where(data1 > data2, np.sqrt(data1-data2), np.sqrt(data2-data2)) which then produces warnings like: /usr/bin/ipython:1: RuntimeWarning: invalid value encountered in sqrt or for the first example: /usr/bin/ipython:1: RuntimeWarning: divide by zero encountered in divide How do I avoid these messages to appear? I know that I could in principle use numpy.seterr. However, I do NOT want to remove these warnings for other potential divide/multiply/sqrt etc errors. Only when I am using a "where", to in fact avoid such warnings! Note that the warnings only happen once, but since I am going to release that code, I would like to avoid the user to get such messages which are irrelevant here (because I am testing, with the where, when NOT to divide by zero or take a sqrt of a negative number). thanks! Eric

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Re: [Numpy-discussion] example reading binary Fortran file
by Neil Martinsen-Burrell 22 Jul '21

22 Jul '21

David Froger <david.froger.info <at> gmail.com> writes: > Hy,My question is about reading Fortran binary file (oh no this question > again...) I've posted this before, but I finally got it cleaned up for the Cookbook. For this purpose I use a subclass of file that has methods for reading unformatted Fortran data. See http://www.scipy.org/Cookbook/FortranIO/FortranFile. I'd gladly see this in numpy or scipy somewhere, but I'm not sure where it belongs. > program makeArray > implicit none > integer,parameter:: nx=10,ny=20 > real(4),dimension(nx,ny):: ux,uy,p > integer :: i,j > open(11,file='uxuyp.bin',form='unformatted') > do i = 1,nx > do j = 1,ny > ux(i,j) = real(i*j) > uy(i,j) = real(i)/real(j) > p (i,j) = real(i) + real(j) > enddo > enddo > write(11) ux,uy > write(11) p > close(11) > end program makeArray When I run the above program compiled with gfortran on my Intel Mac, I can read it back with:: >>> import numpy as np >>> from fortranfile import FortranFile >>> f=FortranFile('uxuyp.bin', endian='<') >>> uxuy = f.readReals(prec='f') # 'f' for default reals >>> len(uxuy) 400 >>> ux = np.array(uxuy[:200]).reshape((20,10)).T >>> uy = np.array(uxuy[200:]).reshape((20,10)).T >>> p = f.readReals('f').reshape((20,10)).T >>> ux array([[ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20.], [ 2., 4., 6., 8., 10., 12., 14., 16., 18., 20., 22., 24., 26., 28., 30., 32., 34., 36., 38., 40.], [ 3., 6., 9., 12., 15., 18., 21., 24., 27., 30., 33., 36., 39., 42., 45., 48., 51., 54., 57., 60.], [ 4., 8., 12., 16., 20., 24., 28., 32., 36., 40., 44., 48., 52., 56., 60., 64., 68., 72., 76., 80.], [ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.], [ 6., 12., 18., 24., 30., 36., 42., 48., 54., 60., 66., 72., 78., 84., 90., 96., 102., 108., 114., 120.], [ 7.,Proxy-Connection: keep-alive Cache-Control: max-age=0 14., 21., 28., 35., 42., 49., 56., 63., 70., 77., 84., 91., 98., 105., 112., 119., 126., 133., 140.], [ 8., 16., 24., 32., 40., 48., 56., 64., 72., 80., 88., 96., 104., 112., 120., 128., 136., 144., 152., 160.], [ 9., 18., 27., 36., 45., 54., 63., 72., 81., 90., 99., 108., 117., 126., 135., 144., 153., 162., 171., 180.], [ 10., 20., 30., 40., 50., 60., 70., 80., 90., 100., 110., 120., 130., 140., 150., 160., 170., 180., 190., 200.]]) >>> uy array([[ 1. , 0.5 , 0.33333334, 0.25 , 0.2 , 0.16666667, 0.14285715, 0.125 , 0.11111111, 0.1 , 0.09090909, 0.08333334, 0.07692308, 0.07142857, 0.06666667, 0.0625 , 0.05882353, 0.05555556, 0.05263158, 0.05 ], [ 2. , 1. , 0.66666669, 0.5 , 0.40000001, 0.33333334, 0.2857143 , 0.25 , 0.22222222, 0.2 , 0.18181819, 0.16666667, 0.15384616, 0.14285715, 0.13333334, 0.125 , 0.11764706, 0.11111111, 0.10526316, 0.1 ], [ 3. , 1.5 , 1. , 0.75 , 0.60000002, 0.5 , 0.42857143, 0.375 , 0.33333334, 0.30000001, 0.27272728, 0.25 , 0.23076923, 0.21428572, 0.2 , 0.1875 , 0.17647059, 0.16666667, 0.15789473, 0.15000001], [ 4. , 2. , 1.33333337, 1. , 0.80000001, 0.66666669, 0.5714286 , 0.5 , 0.44444445, 0.40000001, 0.36363637, 0.33333334, 0.30769232, 0.2857143 , 0.26666668, 0.25 , 0.23529412, 0.22222222, 0.21052632, 0.2 ], [ 5. , 2.5 , 1.66666663, 1.25 , 1. , 0.83333331, 0.71428573, 0.625 , 0.55555558, 0.5 , 0.45454547, 0.41666666, 0.38461539, 0.35714287, 0.33333334, 0.3125 , 0.29411766, 0.27777779, 0.2631579 , 0.25 ], [ 6. , 3. , 2. , 1.5 , 1.20000005, 1. , 0.85714287, 0.75 , 0.66666669, 0.60000002, 0.54545456, 0.5 , 0.46153846, 0.42857143, 0.40000001, 0.375 , 0.35294119, 0.33333334, 0.31578946, 0.30000001], [ 7. , 3.5 , 2.33333325, 1.75 , 1.39999998, 1.16666663, 1. , 0.875 , 0.77777779, 0.69999999, 0.63636363, 0.58333331, 0.53846157, 0.5 , 0.46666667, 0.4375 , 0.41176471, 0.3888889 , 0.36842105, 0.34999999], [ 8. , 4. , 2.66666675, 2. , 1.60000002, 1.33333337, 1.14285719, 1. , 0.8888889 , 0.80000001, 0.72727275, 0.66666669, 0.61538464, 0.5714286 , 0.53333336, 0.5 , 0.47058824, 0.44444445, 0.42105263, 0.40000001], [ 9. , 4.5 , 3. , 2.25 , 1.79999995, 1.5 , 1.28571427, 1.125 , 1. , 0.89999998, 0.81818181, 0.75 , 0.69230771, 0.64285713, 0.60000002, 0.5625 , 0.52941179, 0.5 , 0.47368422, 0.44999999], [ 10. , 5. , 3.33333325, 2.5 , 2. , 1.66666663, 1.42857146, 1.25 , 1.11111116, 1. , 0.90909094, 0.83333331, 0.76923078, 0.71428573, 0.66666669, 0.625 , 0.58823532, 0.55555558, 0.52631581, 0.5 ]]) >>> p array([[ 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.], [ 3., 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22.], [ 4., 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23.], [ 5., 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24.], [ 6., 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.], [ 7., 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26.], [ 8., 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.], [ 9., 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28.], [ 10., 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.], [ 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29., 30.]]) Note that you have to provide the shape information for ux and uy because fortran writes them together as a stream of 400 numbers. -Neil

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Does numpy depend upon a Fortran library?
by Marc F Paterno 11 Apr '19

11 Apr '19

Hello, I have encountered a problem with a binary incompatibility between the Fortran runtime library installed with numpy when using 'pip install --user numpy', and that used by the rest of my program, which is built using gfortran from GCC 8.2. The numpy installation uses libgfortran.5.dylib, and GCC 8.2 provides libgfortran.5.dylib. While investigating the source of this problem, I downloaded the numpy source (https://files.pythonhosted.org/packages/04/b6/d7faa70a3e3eac39f943cc6a6a64c…), and tried building it. The resulting libraries have no coupling to any Fortran library that I can find. I can find no Fortran source code files in the numpy source, except in tests or documentation. I am working on a MacBook laptop, running macOS Mojave, and so am using the Accelerate framework to supply BLAS. I do not understand why the pip installation of numpy includes a Fortran runtime library. Can someone explain to me what I am missing? thanks Marc

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Removing undocumented __buffer__ attribute lookup
by Matti Picus 28 Feb '19

28 Feb '19

In digging around the code, I found a gem in PyArray_FromBuffer (exposed to python as numpy.frombuffer). If a PyObject* does not have a tp_as_buffer->bf_getbuffer function, we check if the python object has a __buffer__ attribute. If so we use that as buf in PyObject_GetBuffer(buf, ...). This seems to stem back to the original numerics code, where getBuffer would look up the attribute and call it as a method. PyArray_FromBuffer does not call the attribute as a method, it simply passes it on to PyObject_GetBuffer, which will then raise an error saying it cannot convert a method. You can try this out by creating a class with a __buffer__ method and calling numpy.frombuffer on it. I submitted a pull request to remove the code. Since it is undocumented and (as far as I can tell) broken, I do not think we need a deprecation cycle. More details, including links to the original numeric code from 2005, in the PR https://github.com/numpy/numpy/pull/13049 Any thoughts or objections? Matti

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[ANN] 12ᵗʰ Advanced Scientific Programming in Python in Camerino, Italy, 2—7 September, 2019
by Tiziano Zito 28 Feb '19

28 Feb '19

12ᵗʰ Advanced Scientific Programming in Python ============================================== a Summer School by the G-Node and the University of Camerino https://python.g-node.org Scientists spend more and more time writing, maintaining, and debugging software. While techniques for doing this efficiently have evolved, only few scientists have been trained to use them. As a result, instead of doing their research, they spend far too much time writing deficient code and reinventing the wheel. In this course we will present a selection of advanced programming techniques and best practices which are standard in the industry, but especially tailored to the needs of a programming scientist. Lectures are devised to be interactive and to give the students enough time to acquire direct hands-on experience with the materials. Students will work in pairs throughout the school and will team up to practice the newly learned skills in a real programming project — an entertaining computer game. We use the Python programming language for the entire course. Python works as a simple programming language for beginners, but more importantly, it also works great in scientific simulations and data analysis. We show how clean language design, ease of extensibility, and the great wealth of open source libraries for scientific computing and data visualization are driving Python to become a standard tool for the programming scientist. This school is targeted at Master or PhD students and Post-docs from all areas of science. Competence in Python or in another language such as Java, C/C++, MATLAB, or R is absolutely required. Basic knowledge of Python and of a version control system such as git, subversion, mercurial, or bazaar is assumed. Participants without any prior experience with Python and/or git should work through the proposed introductory material before the course. We are striving hard to get a pool of students which is international and gender-balanced. Date & Location =============== 2–7 September, 2019. Camerino, Italy. Application =========== You can apply online: https://python.g-node.org/wiki/applications Application deadline: 23:59 UTC, 26 May, 2019. There will be no deadline extension, so be sure to apply on time. Be sure to read the FAQ before applying: https://python.g-node.org/wiki/faq Participation is for free, i.e. no fee is charged! Participants however should take care of travel, living, and accommodation expenses by themselves. Program ======= • Version control with git and how to contribute to open source projects with GitHub • Tidy data analysis and visualization • Testing and debugging scientific code • Advanced NumPy • Organizing, documenting, and distributing scientific code • Advanced scientific Python: context managers and generators • Writing parallel applications in Python • Profiling and speeding up scientific code with Cython and numba • Programming in teams Faculty ======= • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Jenni Rinker, Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark • Juan Nunez-Iglesias, Bioimage Analysis Research Fellow, Monash University, Australia • Nelle Varoquaux, Department of Statistics, UC Berkeley, CA, USA • Pamela Hathway, Neural Reckoning, Imperial College London, UK • Pietro Berkes, NAGRA Kudelski, Lausanne, Switzerland • Rike-Benjamin Schuppner, Institute for Theoretical Biology, Humboldt-Universität zu Berlin, Germany • Stéfan van der Walt, Berkeley Institute for Data Science, UC Berkeley, CA, USA • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany Organizers ========== For the German Neuroinformatics Node of the INCF (G-Node), Germany: • Tiziano Zito, Department of Psychology, Humboldt-Universität zu Berlin, Germany • Caterina Buizza, Personal Robotics Lab, Imperial College London, UK • Zbigniew Jędrzejewski-Szmek, Red Hat Inc., Warsaw, Poland • Jakob Jordan, Department of Physiology, University of Bern, Switzerland For the University of Camerino, Italy: • Barbara Re, Computer Science Division, School of Science and Technology, University of Camerino Italy Website: https://python.g-node.org Contact: python-info(a)g-node.org

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Outreachy internship
by Stefan van der Walt 28 Feb '19

28 Feb '19

Hi everyone, The team at BIDS would like to take on an intern from Outreachy (https://www.outreachy.org), as part of our effort to grow the NumPy developer community. The internship is similar to a GSoC, except that we will pay their salary, as well as do the mentoring. We wanted to check in beforehand to ensure that everyone is comfortable with us doing this on behalf of the NumPy community. Your thoughts and recommendations are welcome! Best regards, Stéfan

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Reminder: weekly status meeting Feb 27 at 12:00 pacific time
by Matti Picus 26 Feb '19

26 Feb '19

The draft agenda is at https://hackmd.io/TtqnUDvPQgaGeej7zR8seA?both# There is a section for community suggested topics, feel free to join the conversation and add in topics that need attention. Past meeting notes can be seen at https://github.com/BIDS-numpy/docs/tree/master/status_meetings The BIDS team

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NumPy 1.16.2 released.
by Charles R Harris 26 Feb '19

26 Feb '19

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.16.2. This is a quick release fixing several problems encountered on Windows. The Python versions supported are 2.7 and 3.5-3.7. The Windows problems addressed are: - DLL load problems for NumPy wheels on Windows, - distutils command line parsing on Windows. There is also a regression fix correcting signed zeros produced by divmod, see the release notes for details. Downstream developers building this release should use Cython >= 0.29.2 and, if using OpenBLAS, OpenBLAS > v0.3.4. If you are installing using pip, you may encounter a problem with older installed versions of NumPy that pip did not delete becoming mixed with the current version, resulting in an ``ImportError``. That problem is particularly common on Debian derived distributions due to a modified pip. The fix is to make sure all previous NumPy versions installed by pip have been removed. See #12736 <https://github.com/numpy/numpy/issues/12736> for discussion of the issue. Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.16.2/>, source archives and release notes are available from Github <https://github.com/numpy/numpy/releases/tag/v1.16.2>. *Contributors* A total of 5 people contributed to this release. People with a "+" by their names contributed a patch for the first time. * Charles Harris * Eric Wieser * Matti Picus * Tyler Reddy * Tony LaTorre + *Pull requests merged* A total of 7 pull requests were merged for this release. * #12909: TST: fix vmImage dispatch in Azure * #12923: MAINT: remove complicated test of multiarray import failure mode * #13020: BUG: fix signed zero behavior in npy_divmod * #13026: MAINT: Add functions to parse shell-strings in the platform-native... * #13028: BUG: Fix regression in parsing of F90 and F77 environment variables * #13038: BUG: parse shell escaping in extra_compile_args and extra_link_args * #13041: BLD: Windows absolute path DLL loading Cheers, Charles Harris

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Stepping down as NumFOCUS liason
by Nathaniel Smith 21 Feb '19

21 Feb '19

Hi all, As part of our relationship with NumFOCUS, we have to have some liasons so they know who to contact etc. So far that's been Ralf and me. But, I haven't had as much time for numpy recently, and I think it would be better to hand this off to someone who's more active. Is anyone interested? Someone on the steering council would probably be most natural. -n

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